‘SENSORIAL LITTER’: BUILDING REFLEXIVE TRUST THROUGH EXAMINING DIGITAL DETRITUS
Bibliographic record
Abstract
How to reflexively examine the ways in which the researcher’s own embodied experiences shape knowledge production is a key question within digital and sensory ethnography. Challenges exist around capturing these embodied experiences through conventional reflexive methods, such as voice and field notes. Furthermore, it can be particularly difficult to capture that which is experienced by the researcher as being ‘too intense’ and resultingly shed or refuse/d by the body; (e.g., fleeting, whirling anxious thoughts; spatial suspensions of disorientation; and voids from pushing away difficult feelings). Nevertheless, as examining refuse/d experiences can focus reflexive engagement on the difficult aspects of fieldwork, this methodological area provides a unique niche for inquiry. In this work, I advance knowledge on retaining and reflecting upon too intense experiences. I first draw from phenomenological embodiment theories to put forward the concept of ‘sensorial litter’. I posit that when shed from the body, intense experiences are not lost, but rather manifest materially through digital media as litter (e.g., anxiety filled texts, emails to supervisors, search histories). Retrieving and examining three pieces of sensorial litter from my own ethnographic work, I then demonstrate how this digital detritus may add critical depth to reflexive engagement with embodiment. Specifically, I illustrate how sensorial litter can provide concrete entry points into the ways in which the researcher’s sensing body is perpetually in flux, shaping and re-shaping throughout fieldwork. I argue that sensorial litter can facilitate reflexive engagement that is many-sited, intertextual, resistant to holism, and perceptive ethnographic research’s inevitable shortcomings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".